POSITION SUMMARY:Natera is a global leader in cell-free DNA testing, serving patients across oncology, women’s health, and organ health. Our Data & AI organization builds the enterprise data platform and AI systems that turn clinical, genomic, and operational data into products that improve patient care and accelerate the business.

We are looking for a Senior Forward Deployed Data Engineer to work directly with internal business stakeholders and domain teams to design, build, and deploy high-impact analytics and AI solutions. Reporting directly to the Data & AI Engineering Tech Lead, you will act as the technical point-of-contact and primary builder for stakeholder initiatives, translating business needs into production-ready data and AI products. This is a hands-on, senior individual contributor role where you will write production code, develop semantic layers, and implement agentic workflows and LLM pipelines.

As a forward deployed engineer, you are AI-native by design—using AI coding harnesses and agentic tooling to accelerate development, testing, and documentation. You will partner closely with the Tech Lead to adopt foundational architecture patterns while rapidly delivering tailored solutions in a regulated environment where data quality, lineage, and privacy are essential.

The ideal candidate combines strong engineering fundamentals with a customer-obsessed mindset, pragmatic problem-solving, and the communication skills needed to bridge complex technical solutions and business outcomes.

Key Responsibilities

Stakeholder Engagement & Technical Ownership

• Embed closely with business stakeholders to deeply understand their data and AI requirements, serving as their primary technical advisor and engineer.
• Run launch discovery and impact analysis for new assays or products (models, views, dashboards touched), documented in Confluence and Jira.
• Translate complex business challenges into clear technical specs and deliverable milestones, partnering with the Tech Lead to align with enterprise architecture standards.
• Adhere to established engineering standards for pipeline design, data modeling, testing, version control (Git/GitLab branching, MRs, PRs) and CI/CD, ensuring deployed solutions are reliable and maintainable.

Hands-On Solutions Engineering

• Design, build, and maintain scalable data pipelines and transformations on our cloud data platform, sourcing clinical, laboratory, operational, and commercial data to serve business analytics.
• Develop governed data models and semantic layers that power self-service analytics, dashboards, and data products (e.g., Patient 360, Provider 360, Test 360) for business teams.
• Develop AI solutions to enable business productivity and automation through use of agentic workflows, RAG/retrieval, and LLM pipelines (extraction/classification)
• Write production-grade SQL and Python; contribute to shared frameworks, templates, and tooling that let the team deliver new analytics solutions faster.
• Ensure the data layer supports performant, trustworthy analytics and LLM querying ; lead root-cause analysis on complex data issues and drive durable fixes.

AI-Native Execution & Continuous Learning

• Leverage AI coding assistants and agentic tools daily across the full software development lifecycle to accelerate solution development and deployment speed.
• Apply AI-native engineering best practices—such as prompt engineering, agentic workflows, and automated testing—to maximize individual output while upholding high quality.
• Collaborate with the Tech Lead and peer engineers through code reviews, technical sharing, and feedback to continually refine forward deployed engineering practices.

Data Quality, Reliability & Compliance

• Implement data quality checks, contracts, lineage, and monitoring so that downstream consumers can trust the data by default.
• Own operational excellence for the team’s solutions: SLAs, incident response, and cost efficiency of compute and storage.
• Ensure pipelines handling PHI and genomic data meet HIPAA, CLIA, and internal security and governance requirements, including access controls, auditability, and retention.

Cross-Functional Partnership

• Work directly with business stakeholders across functions to understand their questions, shape analytics solutions to their needs, and communicate technical tradeoffs in plain language.
• Collaborate with core platform and AI teams under the guidance of the Tech Lead to build solutions on enterprise foundations rather than isolated silos.
• Represent the team in architecture forums, data governance discussions, and tool evaluations.

Qualifications

Required

• 7+ years of data or software engineering experience, with a track record of forward-deployed or customer-facing engineering roles delivering analytics and AI solutions directly to stakeholders.
• Proven ability to partner directly with non-technical business partners, translate technical concepts clearly, and manage project deliverables.
• Demonstrated, hands-on use of AI coding assistants and agentic tools in daily engineering work, with a point of view on how to use them well.
• Expert-level SQL and strong Python, with a track record of shipping and operating production data pipelines and data models at scale.
• Deep experience with a modern cloud data warehouse and cloud infrastructure (Snowflake/AWS preferred).
• Hands-on experience with transformation frameworks (dbt or equivalent) and workflow orchestration (Airflow, Dagster, Prefect, or similar).
• 1-2 years of practical background building with LLM frameworks (LangChain, LangGraph, or equivalent) alongside managed AI services and model routing gateways like Snowflake Cortex, AWS Bedrock, and foundational APIs.
• Strong grounding in analytical data modeling (dimensional or domain-driven), semantic layers, data quality engineering, and CI/CD for data systems.

Preferred

• Experience in healthcare, life sciences, or another regulated domain handling PHI or other sensitive data.
• Experience with lab data management systems, lab informatics systems, bioinformatics pipelines is a plus
• Snowflake experience, including performance tuning, cost management, and features such as dynamic tables, Snowpark, or Cortex.
• Sigma experience, or experience with comparable BI and semantic-layer tools (Looker, Tableau, Power BI), and an understanding of how to design data models for self-service consumption.
• Working knowledge of Power BI, including dataset design, data source connectivity, and troubleshooting dashboards when upstream data changes.
• Experience building and operating agentic or LLM-based workflows within the data development lifecycle.
• Experience with infrastructure-as-code (Terraform) and data observability tooling.
• Experience integrating with Salesforce, laboratory information systems, or EHR data sources.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related quantitative field, or equivalent practical experience.